Nonstationary Bayesian Modeling of Extreme Flood Risk and Return Period Affected by Climate Variables for Xiangjiang River Basin, in South-Central China

The accurate design flood of hydraulic engineering is an important precondition to ensure the safety of residents, and the high precision estimation of flood frequency is a vital perquisite. The Xiangjiang River basin, which is the largest river in Hunan Province of China, is highly inclined to floods. This paper aims to investigate the annual maximum flood peak (AMFP) risk of Xiangjiang River basin under the climate context employing the Bayesian nonstationary time-varying moment models. Two climate covariates, i.e., the average June-July-August Artic Oscillation and sea level pressure in the Northwest Pacific Ocean, are selected and found to exhibit significant positive correlation with AMFP through a rigorous statistical analysis. The proposed models are tested with three cases, namely, stationary, linear-temporal and climate-based conditions. The results both indicate that the climate-informed model demonstrates the best performance as well as sufficiently explain the variability of extreme flood risk. The nonstationary return periods estimated by the expected number of exceedances method are larger than traditional ones built on the stationary assumption. In addition, the design flood could vary with the climate drivers which has great implication when applied in the context of climate change. This study suggests that nonstationary Bayesian modelling with climatic covariates could provide useful information for flood risk management.

Environmental efficiency of Xiangjiang River in China: a data envelopment analysis cross-efficiency approach

PurposeEnvironmental performance becomes a key issue for the sustainable development. Recently, incremental information technology is adopted to collect environmental data and improve environmental performance. Previous environmental efficiency measures mainly focus on individual decision-making units (DMUs). Benefited from the information technology, this paper develops a new environmental efficiency measure to explore the implicit alliances among DMUs and applies it to Xiangjiang River.Design/methodology/approachThis study formulates a new data envelopment analysis (DEA) environmental cross-efficiency measure that considers DMUs' alliances. Each DMUs' alliance is formulated by the DMUs who are supervised by the same manager. In cross-efficiency evaluation context, this paper adopts DMUs' alliances rather than individual DMUs to derive the environmental cross-efficiency measure considering undesirable outputs. Furthermore, the Tobit regression is conducted to analyze the influence of exogenous factors about the environmental cross-efficiency.FindingsThe findings show that (1) Chenzhou performs the best while Xiangtan performed the worst along Xiangjiang River. (2) The environmental efficiency of cities in Xiangjiang River is generally low. Increasing public budgetary expenditure can improve environmental efficiency of cities. (3) The larger the alliance size, the higher environmental efficiency. (4) The income level is negatively correlated with environmental efficiency, indicating that the economy is at the expense of the environment in Xiangjiang River.Originality/valueThis paper contributes to developing a new environmental DEA cross-efficiency measure considering DMUs' alliance, and combining DEA cross-efficiency and Tobit regression in environmental performance measurement of Xiangjiang River. This paper examines the exogenous factors that have influences on environmental efficiency of Xiangjiang River and derive policy implications to improve the sustainable operation.

Research on water environmental capacity accounting of the Yongzhou Section of Xiangjiang River Basin based on the SWAT-EFDC coupling model

In this paper, a coupling model of SWAT (Soil and Water Assessment Tool) and EFDC (Environmental Fluid Dynamics Code) was established, and the relationship between the pollution source and water quality response was identified. Based on the hydrodynamic water quality simulation results and the one-dimensional WEC (water environmental capacity) theoretical formula, the total nitrogen and total phosphorus WEC and the remaining WEC of the Yongzhou Section of Xiangjiang River Basin under the guaranteed rate of 90% and in 2017 were calculated, respectively. It can be seen from the results that the total nitrogen WEC of the Yongzhou Section of Xiangjiang River Basin in 2017 is 27,673.04 t, the total nitrogen WEC under the guaranteed rate of 90% is 19,497.61 t/a and the total phosphorus WEC of the Yongzhou Section of Xiangjiang River Basin in 2017 is 4,877.22 t. The total phosphorus WEC under the guaranteed rate of 90% is 2,936.64 t/a; in 2017, the remaining WECs of total nitrogen and total phosphorus in the entire basin are 14,646.69 and 3,358.67 t, respectively.

Accuracy assessment and error cause analysis of GPM (V06) in Xiangjiang river catchment

Application potential and development prospect of satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) have promising implications. This study discusses causes of spatiotemporal differences on GPM data through the following steps: Initially, calculate bias between satellite-based data and rain gauge data of Xiangjiang river catchment to assess the accuracy of GPM (06E, 06 L, and 06F) products. Second, total errors of satellite precipitation data are divided into hit bias (HBIAS: precipitation detected by both GPM and rain gauge station), missed precipitation (MBIAS: precipitation detected only by rain gauge station), and false precipitation (FBIAS: precipitation detected only by GPM). Third, evaluate the impact of precipitation intensity and total precipitation on accuracy of GPM data and their influence on three error components. Several conclusions are drawn from the results above: (1) Satellite-based precipitation measurements perform better on a larger temporal-spatial scale. (2) The accuracy of TRMM and GPM data displays significant variances on space and time. Season, precipitation intensity, and total precipitation are main factors influencing the accuracy of TRMM and GPM data. (3) The detection capability of satellite products change with seasonal variation and different precipitation intensity level.

Assessing the Risk of Total and Available Potentially Toxic Elements in Agricultural Soil in Typical Mining Areas in Xiangjiang River Basin, Hunan Province

In this study, soil and rice samples from 85 sites in six cities in Hunan Province were analyzed for Cu, Zn, Pb, Cd, Hg, Mn, and Co (total and bioavailable concentrations for soil) in July 2014. The results indicated that the total concentrations of Cu, Zn, Pb, Cd, and Hg in soil had increased significantly compared with the 1980s, and were correlated with their bioavailable concentrations in soil positively. The total concentrations of Cd and Co in soil were correlated with those in rice. Bioavailable concentrations of Cd, Mn, Co, Pb, and Cu represented 64.4%, 33.2%, 12.0%, 11.6%, and 6.1% of the total soil concentrations, respectively. The bioavailable concentrations of Cd and Co in soil had a extremely significant (p < 0.01) positive correlation with those in rice, suggesting that bioavailable concentrations was a better indicator for soil potentially toxic elements contamination. The pH values had a significant influence on the bioavailability of Cd and Cu and the amounts taken up by rice. The Cd contamination in 27.0% rice samples exceeded World Health Organization recommended thresholds. The results added basic pollution distribution data, further revealing the relationships of metals in soil and crops and would offer great help to the metallic pollution control in these areas.

Spatial variation characteristics of vegetation phenology and its influencing factors in the subtropical monsoon climate region of southern China

Understanding the response mechanism of ecosystems to climate change and human disturbance can be improved by analyzing the spatial patterns of vegetation phenology and its influencing factors. Because the diverse phenological patterns are impacted by cloud cover contamination issues in the satellite observations, there are few remote sensing phenological research data in subtropical monsoon climate regions. To better understand the horizontal and vertical changes of vegetation phenology in these regions and how it may be affected by climatic factors and topographical features, we first extracted vegetation phenological information (such as start of growth season (SOS), end of growth season (EOS) and length of growth season (LEN)) from a reconstructed MODIS EVI time-series data. We then used geographic detectors to identify the influencing factors of phenology in different elevation zoning areas. We have found that in the Xiangjiang River Basin: 1) gradual changes in the longitudinal or latitudinal gradient of vegetation phenology were not obvious. Instead of horizontal changes, the variation pattern of phenology was similar to the striped river network of the Xiangjiang River. Earlier SOS mainly appeared in the areas far away from the river; later SOS appeared in the midstream and downstream reaches.2) Elevation played an important role in the regional differentiation of phenology. Boundaries at elevations of 320 m and 520 m distinctly separated the region into plain, hilly, and mountain vegetation phenological characteristics. 3) The impacts of climatic factors were quite different in the three vertical zoning areas. Precipitation was the most crucial factor affecting SOS both in plain and mountain areas. There was no significant factor affecting EOS in the plain area, but temperature had an essential effect on EOS in the mountain area. The hilly areas had a concentrated growth period with no significant factors affecting phenology. These findings highlight the importance of elevation in phenology at a watershed scale, enhance our understanding of the impact of climate changes on subtropical ecosystems, and provide a reference for further land-use change monitoring.